Bootstrap Aggregating (or bagging for short) is a model averaging technique designed to improve the stability and performance of a user-specified base estimator by training a number of them on a unique bootstrapped training set sampled at random with replacement. Bagging works especially well with estimators that tend to have high variance by controlling the variance through averaging.
Data Type Compatibility: Depends on base learner
|1||base||Learner||The base learner.|
|2||estimators||10||int||The number of base learners to train in the ensemble.|
|3||ratio||0.5||float||The ratio of samples from the training set to randomly subsample to train each base learner.|
use Rubix\ML\BootstrapAggregator; use Rubix\ML\Regressors\RegressionTree; $estimator = new BootstrapAggregator(new RegressionTree(10), 300, 0.2);
This meta estimator does not have any additional methods.
- L. Breiman. (1996). Bagging Predictors.